Estimates peg the total number of academic papers and other scholarly literature indexed on the Google Scholar at almost 400 million, making it the world’s largest such database. To compile the index Google surveys hundreds of journals and websites that meet its inclusion guidelines, along with leading conferences in Engineering & Computer Science.
In a blog post last Friday Google released its 2019 Scholar Metrics, designed to “provide an easy way for authors to quickly gauge the visibility and influence of recent articles in scholarly publications …to help authors as they consider where to publish their new research.” One of the top AI conferences – IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ranked in the top 10 for the first time, up from 20th in 2018. The world’s most prominent scientific journals, Nature and Science, ranked first and third respectively.
Google Scholar uses the following as the foundation for the Metrics:
- The h-index of a publication is the largest number h such that at least h articles in that publication were cited at least h times each. For example, a publication with five articles cited by, respectively, 17, 9, 6, 3, and 2, has the h-index of 3.
- The h-core of a publication is a set of top cited h articles from the publication. These are the articles that the h-index is based on. For example, the publication above has the h-core with three articles, those cited by 17, 9, and 6.
- The h-median of a publication is the median of the citation counts in its h-core. For example, the h-median of the publication above is 9. The h-median is a measure of the distribution of citations to the articles in the h-core.
- Finally, the h5-index, h5-core, and h5-median of a publication are, respectively, the h-index, h-core, and h-median of only those of its articles that were published in the last five complete calendar years.
Publications with fewer than 100 articles or that have no citations are not included.
The Scholar Metrics divide publications into various categories (Engineering & Computer Science, Humanities, Literature & Arts, etc.), and display the top 20 publications for each category. It is also possible to view each publication’s top papers.
Take Nature as an example, three of its top-five cited papers are AI-related: Deep Learning by the Turing Award laureates Yann LeCun, Yoshua Bengio, and Geoffrey Hinton; and Human-level control through deep reinforcement learning and Mastering the game of Go with deep neural networks and tree search,both from DeepMind.
Synced notes that 6 well-known AI conferences appear in the Metrics’ overall listing:
- 10. IEEE/CVF Conference on Computer Vision and Pattern Recognition
- 27. Neural Information Processing Systems (NIPS)
- 42. International Conference on Learning Representations
- 56. European Conference on Computer Vision
- 59. International Conference on Machine Learning (ICML)
- 71. IEEE/CVF International Conference on Computer Vision
In the subcategory “Artificial Intelligence,” 7 of 20 on the list are AI conferences:
- 1. Neural Information Processing Systems (NIPS)
- 2. International Conference on Learning Representations
- 3. International Conference on Machine Learning (ICML)
- 7. AAAI Conference on Artificial Intelligence
- 13. International Joint Conference on Artificial Intelligence (IJCAI)
- 16. International Conference on Artificial Intelligence and Statistics
- 20. Conference on Learning Theory (COLT)
The Metrics also include AI-related subcategories such as Computer Vision & Pattern Recognition, Computational Linguistics, Human Computer Interaction, and Robotics.
It should come as no surprise to attendees that AI conferences are publishing so prodigiously — in recent years they have evolved from low-key academic gatherings into extravagant multimedia events attracting thousands and serving as showcases for major innovations and breakthroughs in AI research, development, and deployment.
The 2019 Google Scholar Metrics are available on scholar.google.
Journalist: Fangyu Cai | Editor： Michael Sarazen
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